Beamforming and fronthaul compression design for intelligent reflecting surface aided cloud radio access networks

Owing to the inherent central information processing and resource management ability, the cloud radio access network (C-RAN) is a promising network structure for an intelligent and simplified sixth-generation (6G) wireless network. Nevertheless, to further enhance the capacity and coverage, more rad...

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Vydáno v:	Frontiers of information technology & electronic engineering Ročník 23; číslo 1; s. 31 - 46
Hlavní autoři:	Zhang, Yu, Wu, Xuelu, Peng, Hong, Zhong, Caijun, Chen, Xiaoming
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Hangzhou Zhejiang University Press 01.01.2022 Springer Nature B.V College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China National Mobile Communications Research Laboratory,Southeast University,Nanjing 210096,China%College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China%College of Information Science and Electronic Engineering,Zhejiang University,Hangzhou 310027,China
Témata:	6G mobile communication Algorithms Antennas Beamforming Coding Communication Communications Engineering Computer Hardware Computer Science Computer Systems Organization and Communication Networks Covariance matrix Data processing Design optimization Electrical Engineering Electronics and Microelectronics Energy efficiency Engineering Instrumentation Internet Iterative algorithms Iterative methods Network latency Networks Radio Reconfigurable intelligent surfaces Research Article Resource management Signal processing Uplinking Wireless networks Cloud radio access network (C-RAN) Transmit beamforming 智能反射面云无线接入网传输波束成形 Intelligent reflecting surface (IRS) Fronthaul compression 前传压缩 TN929.5
ISSN:	2095-9184, 2095-9230
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Abstract	Owing to the inherent central information processing and resource management ability, the cloud radio access network (C-RAN) is a promising network structure for an intelligent and simplified sixth-generation (6G) wireless network. Nevertheless, to further enhance the capacity and coverage, more radio remote heads (RRHs) as well as high-fidelity and low-latency fronthaul links are required, which may lead to high implementation cost. To address this issue, we propose to exploit the intelligent reflecting surface (IRS) as an alternative way to enhance the C-RAN, which is a low-cost and energy-efficient option. Specifically, we consider the uplink transmission where multi-antenna users communicate with the baseband unit (BBU) pool through multi-antenna RRHs and multiple IRSs are deployed between the users and RRHs. RRHs can conduct either point-to-point (P2P) compression or Wyner-Ziv coding to compress the received signals, which are then forwarded to the BBU pool through fronthaul links. We investigate the joint design and optimization of user transmit beamformers, IRS passive beamformers, and fronthaul compression noise covariance matrices to maximize the uplink sum rate subject to fronthaul capacity constraints under P2P compression and Wyner-Ziv coding. By exploiting the Arimoto-Blahut algorithm and semi-definite relaxation (SDR), we propose a successive convex approximation approach to solve non-convex problems, and two iterative algorithms corresponding to P2P compression and Wyner-Ziv coding are provided. Numerical results verify the performance gain brought about by deploying IRS in C-RAN and the superiority of the proposed joint design.
AbstractList	Owing to the inherent central information processing and resource management ability, the cloud radio access network (C-RAN) is a promising network structure for an intelligent and simplified sixth-generation (6G) wireless network. Nevertheless, to further enhance the capacity and coverage, more radio remote heads (RRHs) as well as high-fidelity and low-latency fronthaul links are required, which may lead to high implementation cost. To address this issue, we propose to exploit the intelligent reflecting surface (IRS) as an alternative way to enhance the C-RAN, which is a low-cost and energy-efficient option. Specifically, we consider the uplink transmission where multi-antenna users communicate with the baseband unit (BBU) pool through multi-antenna RRHs and multiple IRSs are deployed between the users and RRHs. RRHs can conduct either point-to-point (P2P) compression or Wyner-Ziv coding to compress the received signals, which are then forwarded to the BBU pool through fronthaul links. We investigate the joint design and optimization of user transmit beamformers, IRS passive beamformers, and fronthaul compression noise covariance matrices to maximize the uplink sum rate subject to fronthaul capacity constraints under P2P compression and Wyner-Ziv coding. By exploiting the Arimoto-Blahut algorithm and semi-definite relaxation (SDR), we propose a successive convex approximation approach to solve non-convex problems, and two iterative algorithms corresponding to P2P compression and Wyner-Ziv coding are provided. Numerical results verify the performance gain brought about by deploying IRS in C-RAN and the superiority of the proposed joint design. TN929.5; Owing to the inherent central information processing and resource management ability, the cloud radio access network (C-RAN) is a promising network structure for an intelligent and simplified sixth-generation (6G) wireless network. Nevertheless, to further enhance the capacity and coverage, more radio remote heads (RRHs) as well as high-fidelity and low-latency fronthaul links are required, which may lead to high implementation cost. To address this issue, we propose to exploit the intelligent reflecting surface (IRS) as an alternative way to enhance the C-RAN, which is a low-cost and energy-efficient option. Specifically, we consider the uplink transmission where multi-antenna users communicate with the baseband unit (BBU) pool through multi-antenna RRHs and multiple IRSs are deployed between the users and RRHs. RRHs can conduct either point-to-point (P2P) compression or Wyner-Ziv coding to compress the received signals, which are then forwarded to the BBU pool through fronthaul links. We investigate the joint design and optimization of user transmit beamformers, IRS passive beamformers, and fronthaul compression noise covariance matrices to maximize the uplink sum rate subject to fronthaul capacity constraints under P2P compression and Wyner-Ziv coding. By exploiting the Arimoto-Blahut algorithm and semi-definite relaxation (SDR), we propose a successive convex approximation approach to solve non-convex problems, and two iterative algorithms corresponding to P2P compression and Wyner-Ziv coding are provided. Numerical results verify the performance gain brought about by deploying IRS in C-RAN and the superiority of the proposed joint design.
Author	Wu, Xuelu Peng, Hong Zhong, Caijun Zhang, Yu Chen, Xiaoming
AuthorAffiliation	College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China;National Mobile Communications Research Laboratory,Southeast University,Nanjing 210096,China%College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China%College of Information Science and Electronic Engineering,Zhejiang University,Hangzhou 310027,China
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Author_xml	– sequence: 1 givenname: Yu orcidid: 0000-0002-9736-8244 surname: Zhang fullname: Zhang, Yu email: yzhang@zjut.edu.cn organization: College of Information Engineering, Zhejiang University of Technology, National Mobile Communications Research Laboratory, Southeast University – sequence: 2 givenname: Xuelu surname: Wu fullname: Wu, Xuelu organization: College of Information Engineering, Zhejiang University of Technology – sequence: 3 givenname: Hong surname: Peng fullname: Peng, Hong organization: College of Information Engineering, Zhejiang University of Technology – sequence: 4 givenname: Caijun surname: Zhong fullname: Zhong, Caijun organization: College of Information Science and Electronic Engineering, Zhejiang University – sequence: 5 givenname: Xiaoming surname: Chen fullname: Chen, Xiaoming organization: College of Information Science and Electronic Engineering, Zhejiang University
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Cites_doi	10.1109/LCOMM.2020.3025978 10.1109/MCOM.001.1900107 10.1109/TIT.1972.1054855 10.1109/TCOMM.2019.2940183 10.1080/00401706.1995.10484391 10.1109/LWC.2017.2671431 10.1109/JSAC.2020.3000806 10.1109/LWC.2020.3045884 10.1109/TVT.2012.2226945 10.1109/TSP.2013.2280111 10.1109/TWC.2020.3004330 10.1109/MSP.2014.2330031 10.1109/TSP.2016.2563388 10.1109/LCOMM.2021.3062861 10.1016/S0377-0427(00)00433-7 10.1109/JLT.2014.2382872 10.1109/TSP.2006.872578 10.1109/JSAC.2020.3000802 10.1109/LCOMM.2020.2978394 10.1109/TSP.2020.3019666 10.1109/TWC.2009.081148 10.1109/TCOMM.2020.3042275 10.1109/TSP.2021.3088755 10.1109/LSP.2016.2612192 10.1109/JSAC.2014.2328133 10.1109/ISWCS.2017.8108112 10.13190/j.jbupt.2020-079 10.1109/SPAWC48557.2020.9154243 10.1109/GLOBECOM38437.20199013288 10.1631/FITEE.2210000 10.1109/GLOCOM.2018.8647620
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Keywords	Cloud radio access network (C-RAN) Transmit beamforming 智能反射面云无线接入网传输波束成形 Intelligent reflecting surface (IRS) Fronthaul compression 前传压缩 TN929.5
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Snippet	Owing to the inherent central information processing and resource management ability, the cloud radio access network (C-RAN) is a promising network structure... TN929.5; Owing to the inherent central information processing and resource management ability, the cloud radio access network (C-RAN) is a promising network...
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SubjectTerms	6G mobile communication Algorithms Antennas Beamforming Coding Communication Communications Engineering Computer Hardware Computer Science Computer Systems Organization and Communication Networks Covariance matrix Data processing Design optimization Electrical Engineering Electronics and Microelectronics Energy efficiency Engineering Instrumentation Internet Iterative algorithms Iterative methods Network latency Networks Radio Reconfigurable intelligent surfaces Research Article Resource management Signal processing Uplinking Wireless networks
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Title	Beamforming and fronthaul compression design for intelligent reflecting surface aided cloud radio access networks
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